Effect of Captopril and BQ123 Endothelin-1 Antagonist on Experimentally Induced Hyperlipidemic Nephropathy in Rats.

BACKGROUND: Kidney disease is a universal public health problem, and epidemiological studies demonstrated that the incidences of chronic kidney disease are increasing day by day. However, the efficiency of currently available drugs on the progression of nephropathy is limited. Therefore, the current research was designed to evaluate the therapeutic efficacy of captopril and BQ123 against hyperlipidemia-induced nephropathy in rats. OBJECTIVE: The objective of this study was to examine the implication of Endothelin-1 in experimentally induced hyperlipidemic nephropathy in rats. METHODS: Animals were divided into various groups, and the administration of a high-fat diet for six weeks induced hyperlipidemia. After confirmation of hyperlipidemia, treatment was started for the next 14 days. At the end of the experimental period, the animals were sacrificed, and various biochemical parameters and histopathological studies were performed. RESULTS: Treatment of both the agents in combination effectively decreased BUN levels, serum creatinine, serum nitrite, and proinflammatory markers and ameliorated the pathological injuries to kidneys. CONCLUSION: Furthermore, both treatments also inhibited oxidative stress and restored the hyperlipidemia-induced reduction in the level of antioxidant enzymes.

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.

Background: The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset. Methods: In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy. Results: Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was -522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r2 deep-learning vs. threshold=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r2 deep-learning vs. CCS= 0.80, r2 threshold vs. CCS=0.63). Conclusions: The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: -522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.

Single-dose ethanol intoxication causes acute and lasting neuronal changes in the brain.

Alcohol intoxication at early ages is a risk factor for the development of addictive behavior. To uncover neuronal molecular correlates of acute ethanol intoxication, we used stable-isotope-labeled mice combined with quantitative mass spectrometry to screen more than 2,000 hippocampal proteins, of which 72 changed synaptic abundance up to twofold after ethanol exposure. Among those were mitochondrial proteins and proteins important for neuronal morphology, including MAP6 and ankyrin-G. Based on these candidate proteins, we found acute and lasting molecular, cellular, and behavioral changes following a single intoxication in alcohol-naïve mice. Immunofluorescence analysis revealed a shortening of axon initial segments. Longitudinal two-photon in vivo imaging showed increased synaptic dynamics and mitochondrial trafficking in axons. Knockdown of mitochondrial trafficking in dopaminergic neurons abolished conditioned alcohol preference in Drosophila flies. This study introduces mitochondrial trafficking as a process implicated in reward learning and highlights the potential of high-resolution proteomics to identify cellular mechanisms relevant for addictive behavior.

Improved spatial resolution by induced live cell and organelle swelling in hypotonic solutions.

Induced morphology changes of cells and organelles are by far the easiest way to determine precise protein sub-locations and organelle quantities in light microscopy. By using hypotonic solutions to swell mammalian cell organelles we demonstrate that precise membrane, lumen or matrix protein locations within the endoplasmic reticulum, Golgi and mitochondria can reliably be established. We also show the benefit of this approach for organelle quantifications, especially for clumped or intertwined organelles like peroxisomes and mitochondria. Since cell and organelle swelling is reversible, it can be applied to live cells for successive high-resolution analyses. Our approach outperforms many existing imaging modalities with respect to resolution, ease-of-use and cost-effectiveness without excluding any co-utilization with existing optical (super)resolution techniques.

Tracking Virus Particles in Fluorescence Microscopy Images Using Multi-Scale Detection and Multi-Frame Association.

Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.

Curcumin suppresses gelatinase B mediated norepinephrine induced stress in H9c2 cardiomyocytes.

BACKGROUND: Extracellular matrix (ECM) remodeling facilitates biomechanical signals in response to abnormal physiological conditions. This process is witnessed as one of the major effects of the stress imposed by catecholamines, such as epinephrine and norepinephrine (NE), on cardiac muscle cells. Matrix metalloproteinases (MMPs) are the key proteases involved in degradation of the ECM in heart. OBJECTIVES: The present study focuses on studying the effect of curcumin on Gelatinase B (MMP-9), an ECM remodeling regulatory enzyme, in NE-induced cardiac stress. Curcumin, a bioactive polyphenol found in the spice turmeric, has been studied for its multi-fold beneficial properties. This study focuses on investigating the role of curcumin as a cardio-protectant. METHODS: H9c2 cardiomyocytes were subjected to NE and curcumin treatments to study the response in stress conditions. Effect on total collagen content was studied using Picrosirus red staining. Gelatinase B activity was assessed through Gel-Diffusion Assay and Zymographic techniques. RT-PCR, Western Blotting and Immunocytochemistry were performed to study effect on expression of gelatinase B. Further, the effect of curcumin on the localization of NF-κB, known to regulate gelatinase B, was also examined. RESULTS: Curcumin suppressed the increase in the total collagen content under hypertrophic stress and was found to inhibit the in-gel and in-situ gelatinolytic activity of gelatinase B. Moreover, it was found to suppress the mRNA and protein expression of gelatinase B. CONCLUSIONS: The study provides an evidence for an overall inhibitory effect of curcumin on Gelatinase B in NE-induced hypertrophic stress in H9c2 cardiomyocytes which may contribute in the prevention of ECM remodeling.

Cell in situ zymography: an in vitro cytotechnology for localization of enzyme activity in cell culture.

In situ zymography is a unique technique for detection and localization of enzyme-substrate interactions majorly in histological sections. Substrate with quenched fluorogenic molecule is incorporated in gel over which tissue sections are mounted and then incubated in buffer. The enzymatic activity is observed in the form of fluorescent signal. With the advancements in the field of biological research, use of in vitro cell culture has become very popular and holds great significance in multiple fields including inflammation, cancer, stem cell biology and the still emerging 3-D cell cultures. The information on analysis of enzymatic activity in cell lines is inadequate presently. We propose a single-step methodology that is simple, sensitive, cost-effective, and functional to perform and study the 'in position' activity of enzyme on substrate for in vitro cell cultures. Quantification of enzymatic activity to carry out comparative studies on cells has also been illustrated. This technique can be applied to a variety of enzyme classes including proteases, amylases, xylanases, and cellulases in cell cultures.

Analysis of human collagen sequences.

The extracellular matrix is fast emerging as important component mediating cell-cell interactions, along with its established role as a scaffold for cell support. Collagen, being the principal component of extracellular matrix, has been implicated in a number of pathological conditions. However, collagens are complex protein structures belonging to a large family consisting of 28 members in humans; hence, there exists a lack of in depth information about their structural features. Annotating and appreciating the functions of these proteins is possible with the help of the numerous biocomputational tools that are currently available. This study reports a comparative analysis and characterization of the alpha-1 chain of human collagen sequences. Physico-chemical, secondary structural, functional and phylogenetic classification was carried out, based on which, collagens 12, 14 and 20, which belong to the FACIT collagen family, have been identified as potential players in diseased conditions, owing to certain atypical properties such as very high aliphatic index, low percentage of glycine and proline residues and their proximity in evolutionary history. These collagen molecules might be important candidates to be investigated further for their role in skeletal disorders.

Comparative analysis of human matrix metalloproteinases: Emerging therapeutic targets in diseases.

The identification of specific target proteins for any diseased condition involves extensive characterization of the potentially involved proteins. Members of a protein family demonstrating comparable features may show certain unusual features when implicated in a pathological condition. Advancements in the field of computational biology and the use of various bioinformatics tools for analysis can aid researchers to comprehend their system of work in primary stages of research. This initial screening can help to reduce time and cost of testing and experimentation in laboratory. Human matrix metalloproteinase (MMP) family of endopeptidases is one such family of 23 members responsible for the remodeling of extracellular matrix (ECM) by degradation of the ECM proteins. Though their role has been implicated in various pathological conditions such as arthritis, atherosclerosis, cancer, liver fibrosis, cardio-vascular and neurodegenerative disorders, little is known about the specific involvement of members of the large MMP family in diseases. A comparative in silico characterization of the MMP protein family has been carried out to analyze their physico-chemical, secondary structural and functional properties. Based on the observed patterns of occurrence of atypical features, we hypothesize that cysteine rich and highly thermostable MMPs might be key players in diseased conditions. Thus, a plausible grouping of disease responsive MMPs that might be considered as promising clinical targets may be done. This study can be used as a fundamental approach to characterize, analyze and screen large protein families for the identification of signature patterns.